Poker is in the family of imperfect information games unlike other games such as chess, connect four, etc which are perfect information game instead. While many perfect information games have been solved, no non-trivial imperfect information game has been solved to date. This makes poker a great test bed for Artificial Intelligence research. In this paper we firstly compare Game theory optimal poker to Exploitative poker. Secondly, we discuss the intricacies of abstraction techniques, betting models, and specific strategies employed by successful poker bots like Tartanian[1] and Pluribus[6]. Thirdly, we also explore 2-player vs multi-player games and the limitations that come when playing with more players. Finally, this paper discusses the role of machine learning and theoretical approaches in developing winning strategies and suggests future directions for this rapidly evolving field.

翻译：扑克属于不完美信息博弈家族，与象棋、四子棋等完美信息博弈不同。尽管许多完美信息博弈已被求解，但至今尚无任何非平凡的不完美信息博弈得到完全解决。这使得扑克成为人工智能研究的理想试验平台。本文首先比较了博弈论最优扑克与剥削性扑克；其次，我们讨论了抽象技术、下注模型以及Tartanian[1]和Pluribus[6]等成功扑克机器人所采用的具体策略的复杂性；第三，我们探讨了双人对战与多人博弈及其参与者增多带来的局限性；最后，本文论述了机器学习与理论方法在开发获胜策略中的作用，并提出了这一快速演进领域的未来方向。